Data Analyst & AI Developer Engneer

• Software Development – Design, develop, implement, and test application software; contribute through coding, algorithm design, debugging, and system integration.
• Data Collection & Management – Acquire, clean, and organize structured and unstructured data from diverse sources (databases, APIs, logs, external datasets).
• Data Analysis & Visualization – Conduct statistical analyses, generate reports, and create interactive dashboards (Power BI, Tableau, Excel) to support strategic decision-making.
• Database Design & Optimization – Define requirements, design efficient database schemas, and optimize queries for scalability and performance.
• Machine Learning & AI Development – Build, train, and evaluate predictive and prescriptive models using Python (TensorFlow, PyTorch).
• Deployment & Automation – Deploy and integrate AI/ML models into production environments (APIs, pipelines, MLOps).
• Cloud & Big Data Solutions – Leverage cloud platforms (AWS, Azure, GCP) and big data frameworks (Spark, Hadoop) for advanced analytics.
• Business Problem-Solving – Collaborate with stakeholders to translate business requirements into analytical models and AI-driven solutions.
• Innovation & Research – Stay abreast of emerging trends in AI, data science, cloud, and automation, applying them to optimize business processes.
• Documentation & Reporting – Prepare technical documentation, research reports, and user manuals for developed systems and solutions.
• Collaboration & Mentorship – Work cross-functionally with IT, business, and operations teams; mentor junior staff in data analytics and AI development.
• Technical Support – Serve as a technical advisor to other departments, providing training and knowledge transfer when needed.
• Additional Duties – Perform other responsibilities as assigned by management.

Minimum Criteria

• Education: Bachelor’s degree in IT, Computer Science, AI, or related fields (top universities preferred).
• Programming: Python (TensorFlow, PyTorch), C#, ASP.Net; basic R/Java/Scala.
• Data: SQL, basic NoSQL; ETL & large dataset handling.
• Analytics: Excel (advanced), Power BI, Tableau; solid statistical knowledge.
• AI/ML: Machine Learning, Deep Learning, Cloud & Big Data (AWS, Azure, GCP, Spark, Hadoop).
• Database: Design & optimization.
• Math: Linear Algebra, Calculus, Probability, Optimization.
• Others: Problem-solving, adaptability, English proficiency, teamwork & communication skills.
• Age: 23–40.

Desirable Criteria

• 1+ years in data/AI/ML roles.
• Experience with MLOps, APIs, NLP, Computer Vision, or Generative AI.
• Hands-on with cloud AI services.
• Domain exposure (Manufacturing, Supply Chain, Finance).
• Relevant certifications (Power BI, Google Data Analytics, AWS/Azure/Google ML).
• Participation in Kaggle, research, or open-source AI projects.